diff --git a/llama-cpp-2/src/context/sample/sampler.rs b/llama-cpp-2/src/context/sample/sampler.rs
index 74906a7c..cfe90499 100644
--- a/llama-cpp-2/src/context/sample/sampler.rs
+++ b/llama-cpp-2/src/context/sample/sampler.rs
@@ -54,8 +54,10 @@ pub type SampleFinalizer<C> = dyn Fn(LlamaTokenDataArray, &mut C) -> Vec<LlamaTo
 
 /// A series of sampling steps that will produce a vector of token data.
 ///
-/// `C` is dynamic context that will be passed to the sampling functions. I expect `C` will
-/// often be [`()`], [`crate::context::LlamaContext`] or a token history (or some combination of these).
+/// `C` is dynamic context that will be passed to the sampling functions. Some sampling steps may
+/// require state to be maintained across multiple samples, and this context can be used to store
+/// that state. For example, [`LlamaTokenDataArray::sample_token_mirostat_v2`] requires a `mu` to be
+/// shared across multiple samples.
 pub struct Sampler<'a, C> {
     /// The steps to take when sampling.
     pub steps: Vec<&'a SampleStep<C>>,
diff --git a/llama-cpp-2/src/token/data_array.rs b/llama-cpp-2/src/token/data_array.rs
index 0f89d59f..776d222a 100644
--- a/llama-cpp-2/src/token/data_array.rs
+++ b/llama-cpp-2/src/token/data_array.rs
@@ -344,4 +344,34 @@ impl LlamaTokenDataArray {
             });
         }
     }
+
+    ///  Mirostat 2.0 algorithm described in the [paper](https://arxiv.org/abs/2007.14966). Uses tokens instead of words.
+    ///
+    /// # Parameters
+    ///
+    /// * `tau`  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
+    /// * `eta` The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
+    /// * `mu` Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
+    pub fn sample_token_mirostat_v2(
+        &mut self,
+        ctx: &mut LlamaContext,
+        tau: f32,
+        eta: f32,
+        mu: &mut f32,
+    ) -> LlamaToken {
+        let mu_ptr = ptr::from_mut(mu);
+        let token = unsafe {
+            self.modify_as_c_llama_token_data_array(|c_llama_token_data_array| {
+                llama_cpp_sys_2::llama_sample_token_mirostat_v2(
+                    ctx.context.as_ptr(),
+                    c_llama_token_data_array,
+                    tau,
+                    eta,
+                    mu_ptr,
+                )
+            })
+        };
+        *mu = unsafe { *mu_ptr };
+        LlamaToken(token)
+    }
 }